Smoothed Dissipative Particle Dynamics model for mesoscopic multiphase flows in the presence of thermal fluctuations

Thermal fluctuations cause perturbations of fluid-fluid interfaces and highly nonlinear hydrodynamics in multiphase flows. In this work, we develop a novel multiphase smoothed dissipative particle dynamics model. This model accounts for both bulk hydrodynamics and interfacial fluctuations. Interfacial surface tension is modeled by imposing a pairwise force between SDPD particles. We show that the relationship between the model parameters and surface tension, previously derived under the assumption of zero thermal fluctuation, is accurate for fluid systems at low temperature but overestimates the surface tension for intermediate and large thermal fluctuations. To analyze the effect of thermal fluctuations on surface tension, we construct a coarse-grained Euler lattice model based on the mean field theory and derive a semi-analytical formula to directly relate the surface tension to model parameters for a wide range of temperatures and model resolutions. We demonstrate that the present method correctly models the dynamic processes, such as bubble coalescence and capillary spectra across the interface

Thermal Fluctuations in a Lamellar Phase of a Binary Amphiphile-Solvent Mixture: A Molecular Dynamics Study

We investigate thermal fluctuations in a smectic A phase of an amphiphile-solvent mixture with molecular dynamics simulations. We use an idealized model system, where solvent particles are represented by simple beads, and amphiphiles by bead-and-spring tetramers. At a solvent bead fraction of 20 % and sufficiently low temperature, the amphiphiles self-assemble into a highly oriented lamellar phase. Our study aims at comparing the structure of this phase with the predictions of the elastic theory of thermally fluctuating fluid membrane stacks [Lei et al., J. Phys. II 5, 1155 (1995)]. We suggest a method which permits to calculate the bending rigidity and compressibility modulus of the lamellar stack from the simulation data. The simulation results are in reasonable agreement with the theory

Evaluation of social personalized adaptive E-Learning environments : end-user point of view

The use of adaptations, along with the social affordances of collaboration and networking, carries a great potential for improving e-learning experiences. However, the review of the previous work indicates current e-learning systems have only marginally explored the integration of social features and adaptation techniques. The overall aim of this research, therefore, is to address this gap by evaluating a system developed to foster social personalized adaptive e-learning experiences. We have developed our first prototype system, Topolor, based on the concepts of Adaptive Educational Hypermedia and Social E-Learning. We have also conducted an experimental case study for the evaluation of the prototype system from different perspectives. The results show a considerably high satisfaction of the end users. This paper reports the evaluation results from end user point of view, and generalizes our method to a component-based evaluation framework

Dynamic phenomena in superconducting oxides by ESR

Dynamic electron spin resonance (ESR) measurements compare the paramagnetic and antiferromagnetic (AF) properties of superconducting oxides in the range 4 K to room temperature, at 8 MHz and 9.36 GHz. Two are derivatives of YBa2Cu30 7: 1: Nd(Nd0.05Ba0.95 )2Cu30 7, Te0 =72 K and II: Y0.2Cao.8Sr2[Cu2(Tlo.5Pb0.5 )]07, Te0 =108 K and two are cases where AF ordering dominates the weak superconductivity: III: Nb01.1\u3e 1. 25 ~Teo~ 10 K and IV: La2Ni04.00, 70 K :::: Teo:::: 40 K. At temperatures 298:::: T:::: 64 K, the ESR absorption by I indicates orthorhombic symmetry. The peaks at Ke =2.06, gb =2.13, and Ka =2.24 are identified with the presence of 5% Nd3+( 41912 ) in the Ba layer because the characteristic Cu2+ impurity hyperfine structure is absent and the ESR signal disappears several degrees below Te. Near Te the ESR absorption is reduced by two orders of magnitude. Proximity effects give rise to interference fringes with period r1 ( T) independent of the field B and the rate of sweep dBzldt. ESR is observed below Te because flux penetrates the superconductor. The temperature dependence of r1 leads to an activation energy for the flux motion E0 (1)/R ~ 16 K and Ea (111)/R ~3 K =Te /4. In the superconducting state a coherent flux expulsion response to a change in B. from 500 mT to zero is observed in times T, = 8 to 10 s. The inverse rate of noise spikes due to flux expulsion, when the samples are cooled through Te in a magnetic field, varies from Tnoise=3.5 s for III to 21 s for IV. The microwave absorption spectra identify three temperature regimes: (i) For 3.5 K \u3c T \u3c T m T* \u3c Teo superconducting behavior was confirmed by the energy loss near zero magnetic field and the kinetics of high-field noise due to flux expulsion. Near g =2.00 ESR absorption is observed for all materials. A broad absorption near 50 to 100 mT at 9.36 GHz has been attributed to AF resonance. (ii) T m T* ~ T ~ Te identifies the range where flux motion gives rise to interference fringes in the ESR absorption. (iii) ESR and AF resonance are observed immediately after warming above Tc

Privacy-Preserving Outsourcing of Large-Scale Nonlinear Programming to the Cloud

The increasing massive data generated by various sources has given birth to big data analytics. Solving large-scale nonlinear programming problems (NLPs) is one important big data analytics task that has applications in many domains such as transport and logistics. However, NLPs are usually too computationally expensive for resource-constrained users. Fortunately, cloud computing provides an alternative and economical service for resource-constrained users to outsource their computation tasks to the cloud. However, one major concern with outsourcing NLPs is the leakage of user's private information contained in NLP formulations and results. Although much work has been done on privacy-preserving outsourcing of computation tasks, little attention has been paid to NLPs. In this paper, we for the first time investigate secure outsourcing of general large-scale NLPs with nonlinear constraints. A secure and efficient transformation scheme at the user side is proposed to protect user's private information; at the cloud side, generalized reduced gradient method is applied to effectively solve the transformed large-scale NLPs. The proposed protocol is implemented on a cloud computing testbed. Experimental evaluations demonstrate that significant time can be saved for users and the proposed mechanism has the potential for practical use.Comment: Ang Li and Wei Du equally contributed to this work. This work was done when Wei Du was at the University of Arkansas. 2018 EAI International Conference on Security and Privacy in Communication Networks (SecureComm

Shot noise of inelastic tunneling through quantum dot systems

We present a theoretical analysis of the effect of inelastic electron scattering on current and its fluctuations in a mesoscopic quantum dot (QD) connected to two leads, based on a recently developed nonperturbative technique involving the approximate mapping of the many-body electron-phonon coupling problem onto a multichannel single-electron scattering problem. In this, we apply the B\"uttiker scattering theory of shot noise for a two-terminal mesoscopic device to the multichannel case with differing weight factors and examine zero-frequency shot noise for two special cases: (i) a single-molecule QD and (ii) coupled semiconductor QDs. The nonequilibrium Green's function method facilitates calculation of single-electron transmission and reflection amplitudes for inelastic processes under nonequilibrium conditions in the mapping model. For the single-molecule QD we find that, in the presence of the electron-phonon interaction, both differential conductance and differential shot noise display additional peaks as bias-voltage increases due to phonon-assisted processes. In the case of coupled QDs, our nonperturbative calculations account for the electron-phonon interaction on an equal footing with couplings to the leads, as well as the coupling between the two dots. Our results exhibit oscillations in both the current and shot noise as functions of the energy difference between the two QDs, resulting from the spontaneous emission of phonons in the nonlinear transport process. In the "zero-phonon" resonant tunneling regime, the shot noise exhibits a double peak, while in the "one-phonon" region, only a single peak appears.Comment: 10 pages, 6 figures, some minor changes, accepted by Phys. Rev.

Position dependent photodetector from large area reduced graphene oxide thin films

We fabricated large area infrared photodetector devices from thin film of chemically reduced graphene oxide (RGO) sheets and studied their photoresponse as a function of laser position. We found that the photocurrent either increases, decreases or remain almost zero depending upon the position of the laser spot with respect to the electrodes. The position sensitive photoresponse is explained by Schottky barrier modulation at the RGO film-electrode interface. The time response of the photocurrent is dramatically slower than single sheet of graphene possibly due to disorder from the chemically synthesis and interconnecting sheets

Search for IR Emission from Intracluster Dust in A2029

We have searched for IR emission from the intracluster dust (ICD) in the galaxy cluster A2029. Weak signals of enhanced extended emission in the cluster are detected at both 24 and 70 micron. However, the signals are indistinguishable from the foreground fluctuations. The 24 versus 70 micron color map does not discriminate the dust emission in the cluster from the cirrus emission. After excluding the contamination from the point sources, we obtain upper limits for the extended ICD emission in A2029, 5 x 10^3 Jy/sr at 24 micron and 5 x 10^4 Jy/sr at 70 micron. The upper limits are generally consistent with the expectation from theoretical calculations and support a dust deficiency in the cluster compared to the ISM in our galaxy. Our results suggest that even with the much improved sensitivity of current IR telescopes, a clear detection of the IR emission from ICD may be difficult due to cirrus noise.Comment: 5 pages, 4 figures, accepted by ApJ

GaAs-based Self-Aligned Stripe Superluminescent Diodes Processed Normal to the Cleaved Facet

We demonstrate GaAs-based superluminescent diodes (SLDs) incorporating a window-like back facet in a self-aligned stripe. SLDs are realised with low spectral modulation depth (SMD) at high power spectral density, without application of anti-reflection coatings. Such application of a window-like facet reduces effective facet reflectivity in a broadband manner. We demonstrate 30mW output power in a narrow bandwidth with only 5% SMD, outline the design criteria for high power and low SMD, and describe the deviation from a linear dependence of SMD on output power as a result of Joule heating in SLDs under continuous wave current injection. Furthermore, SLDs processed normal to the facet demonstrate output powers as high as 20mW, offering improvements in beam quality, ease of packaging and use of real estate. © (2016) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only

Differentially Private Model Selection with Penalized and Constrained Likelihood

In statistical disclosure control, the goal of data analysis is twofold: The released information must provide accurate and useful statistics about the underlying population of interest, while minimizing the potential for an individual record to be identified. In recent years, the notion of differential privacy has received much attention in theoretical computer science, machine learning, and statistics. It provides a rigorous and strong notion of protection for individuals' sensitive information. A fundamental question is how to incorporate differential privacy into traditional statistical inference procedures. In this paper we study model selection in multivariate linear regression under the constraint of differential privacy. We show that model selection procedures based on penalized least squares or likelihood can be made differentially private by a combination of regularization and randomization, and propose two algorithms to do so. We show that our private procedures are consistent under essentially the same conditions as the corresponding non-private procedures. We also find that under differential privacy, the procedure becomes more sensitive to the tuning parameters. We illustrate and evaluate our method using simulation studies and two real data examples